Noninvasive assessment and classification of human skin burns using images of Caucasian and African patients

Abubakar, Aliyu; Ugail, Hassan; Bukar, Ali Maina

doi:10.1117/1.jei.29.4.041002

Cited by 12 publications

(17 citation statements)

References 26 publications

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“…Datasets Classification Accuracy ResNet101 + SVM [9] Burns & Healthy Skin (Caucasian) 99.5% VGG-16 + SVM [17] Burns & Healthy Skin (Caucasian) 99.3% Moreover, though studies in the literature were mainly based on binary discrimination between burn and healthy skin, comparing our finding as presented in Table 6 shows impressive outcome despite addressing most complicated issue (burns and other skin injuries). Table 6.…”

Section: Features and Classifiermentioning

confidence: 66%

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Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar

Ajuji

Yahya

2020

ASI

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While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use transfer learning by leveraging pre-trained deep learning models due to deficient dataset in this paper, to discriminate two classes of skin injuries—burnt skin and injured skin. Experiments were extensively conducted using three state-of-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies—fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy in categorizing burnt skin ad injured skin of approximately 99.9%.

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Section: Features and Classifiermentioning

confidence: 66%

“…ResNet101 + SVM [9] Burns & Healthy Skin (Caucasian) 99.5% VGG-16 + SVM [17] Burns & Healthy Skin (Caucasian) 99.3% VGG-19 + SVM [17] Burns & Healthy Skin (Caucasian) 98.3% VGG-Face + SVM [17] Burns & Healthy Skin (Caucasian) 96.3% ResNet152 + SVM Burns & PUB (Caucasian) 99.9%…”

Section: Features and Classifier Datasets Classification Accuracymentioning

confidence: 99%

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar

Ajuji

Yahya

2020

ASI

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“…Studies by [17] proposed the use of off-the-shelf features with SVM classifier to classify burns in both or some sort of skin abnormalities. This is the gap we propose to address in this paper using pretrained deep learning models.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar¹,

Ajuji²,

Yahya³

2020

Preprint

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While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities, and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use pre-trained deep learning models due to deficient dataset to train a new model from scratch. Experiments were extensively conducted using three stateof-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies: fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers, and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy of approximately 99.9%.

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“…Study referenced [18] proposed another binary classification of burns using deep neural network features and support vector machinesthe. In this study, three deep CNN models were used; two of the models (VGG16 and VGG19) were training on ImageNet database to categorize 1000 different objects and the other model was trained to recognize human faces (VGGFace).…”

Section: Literaturementioning

confidence: 99%

“…Therefore, two pre-trained ImageNet CNNs (VGG16 and ResNet50) are used for feature extraction in this study. The choice was inspired by the fact that CNN models trained on multiple data categories have strong generic information that can be used on the fly for image feature representation [18,20,27].…”

Section: Choice Of a Feature Extractormentioning

confidence: 99%

Burns Depth Assessment Using Deep Learning Features

et al. 2020

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Purpose Burns depth evaluation is a lifesaving task and very challenging that requires objective techniques to accomplish. While the visual assessment is the most commonly used by surgeons, its accuracy reliability ranges between 60 and 80% and subjective that lacks any standard guideline. Currently, the only standard adjunct to clinical evaluation of burn depth is Laser Doppler Imaging (LDI) which measures microcirculation within the dermal tissue, providing the burns potential healing time which correspond to the depth of the injury achieving up to 100% accuracy. However, the use of LDI is limited due to many factors including high affordability and diagnostic costs, its accuracy is affected by movement which makes it difficult to assess paediatric patients, high level of human expertise is required to operate the device, and 100% accuracy possible after 72 h. These shortfalls necessitate the need for objective and affordable technique. Method In this study, we leverage the use of deep transfer learning technique using two pretrained models ResNet50 and VGG16 for the extraction of image patterns (ResFeat50 and VggFeat16) from a a burn dataset of 2080 RGB images which composed of healthy skin, first degree, second degree and third-degree burns evenly distributed. We then use One-versus-One Support Vector Machines (SVM) for multi-class prediction and was trained using 10-folds cross validation to achieve optimum trade-off between bias and variance. Results The proposed approach yields maximum prediction accuracy of 95.43% using ResFeat50 and 85.67% using VggFeat16. The average recall, precision and F1-score are 95.50%, 95.50%, 95.50% and 85.75%, 86.25%, 85.75% for both ResFeat50 and VggFeat16 respectively. Conclusion The proposed pipeline achieved a state-of-the-art prediction accuracy and interestingly indicates that decision can be made in less than a minute whether the injury requires surgical intervention such as skin grafting or not.

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Noninvasive assessment and classification of human skin burns using images of Caucasian and African patients

Cited by 12 publications

References 26 publications

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Burns Depth Assessment Using Deep Learning Features

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